Identification of switched Markov autoregressive eXogenous systems with hidden switching state

Identification of the Switched Markov Autoregressive eXogenous (ARX) systems is considered in this paper. With a Markov chain model governing the evolution of the hidden switching state, a Switched Markov ARX System (SMARX) is formulated and a solution strategy is proposed. The Expectation–Maximization (EM) algorithm is employed in the identification of the SMARX systems in which both a Hidden Markov Model (HMM) for the discrete-valued switching dynamics and local ARX models for continuous dynamics are estimated. Through the comparison between the proposed method and previous switched ARX system identification methods, it is shown that by modeling both the switching and continuous dynamics, the accuracy of the identification results can, to various extent, be improved.